In the framework of the RECCAP2 initiative, we present the greenhouse gas (GHG) and carbon (C) budget of Europe. For the decade of the 2010s, we present a bottom-up (BU) estimate of GHG net-emissions of 3.9 Pg CO2-eq. yr-1 (global warming potential on 100 year horizon), and are largely dominated by fossil fuel emissions. In this decade, terrestrial ecosystems are a net GHG sink of 0.9 Pg CO2-eq. yr-1, dominated by a CO2 sink. For CH4 and N2O, we find good agreement between BU and top-down (TD) estimates from atmospheric inversions. However, our BU land CO2 sink is significantly higher than TD estimates. We further show that decadal averages of GHG net-emissions have declined by 1.2 Pg CO2-eq. yr-1 since the 1990s, mainly due to a reduction in fossil fuel emissions. In addition, based on both data driven BU and TD estimates, we also find that the land CO2 sink has weakened over the past two decades. In particular, we identified a decreasing sink strength over Scandinavia, which can be attributed to an intensification of forest management. These are partly offset by increasing CO2 sinks in parts of Eastern Europe and Northern Spain, attributed in part to land use change. Extensive regions of high CH4 and N2O emissions are mainly attributed to agricultural activities and are found in Belgium, the Netherlands and the southern UK. We further analyzed interannual variability in the GHG budgets. The drought year of 2003 shows the highest net-emissions of CO2 and of all GHGs combined.

East Asia (China, Japan, Koreas and Mongolia) has been the world’s economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid-century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long-lived greenhouse gases (CO2, CH4 and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint bottom-up and top-down approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between 196.9 ± 527.0 Tg CO2eq yr-1 (the top-down approach) and -20.8 ± 205.5 Tg CO2eq yr-1 (the bottom-up approach) during 2000-2019. This net GHG emission includes a large land CO2 sink (-1251.3 ± 456.9 Tg CO2 yr-1 based on the top-down approach and -1356.1 ± 155.6 Tg CO2 yr-1 based on the bottom-up approach), which is being fully offset by biogenic CH4 and N2O emissions, predominantly coming from the agricultural sector. Emerging data sources and modelling capacities have helped achieve agreement between the top-down and bottom-up approaches to within 20% for all three GHGs, but sizeable uncertainties remain in several flux terms. For example, the reported CO2 flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr-1 to a net sink of ~-700 Tg CO2 yr-1.

As part of the REgional Carbon Cycle Assessment and Processes Phase 2 (RECCAP2) project, we developed a comprehensive African Greenhouse gases (GHG) budget for the period 2010-2019 and compared it to the budget over the 1985-2009 (RECCAP1) period. We considered bottom-up process-based models, data-driven remotely sensed products, and national GHG inventories in comparison with top-down atmospheric inversions, accounting also for lateral fluxes. We incorporated emission estimates derived from novel methodologies for termites, herbivores, and fire, which are particularly important in Africa. We further constrained global woody biomass change products with high-quality regional observations. During the RECCAP2 period, Africa’s carbon sink capacity is decreasing, with net ecosystem exchange switching from a small sink of −0.61 ± 0.58 PgCyr−1 in RECCAP1 to a small source in RECCAP2 at 0.162 (-1.793/2.633) PgCyr-1. Net CO2 emissions estimated from bottom-up approaches were 1.588 (-6.461/11.439) PgCO2yr-1, net CH4 were 78.453 (36.665/59.677) TgCH4yr-1) and net N2O were 1.81 (1.716/2.239) TgN2Oyr-1. Top-down atmospheric inversions showed similar trends. LUC emissions increased, representing one of the largest contributions at 1.746 (0.841/2.651) PgCO2eq yr-1 to the African GHG budget and almost similar to emissions from fossil fuels at 1.743 (1.531/1.956) PgCO2eq yr-1, which also increased from RECCAP1. Additionally, wildfire emissions decreased, while fuelwood burning increased. For most component fluxes, uncertainty is large, highlighting the need for increased efforts to address Africa-specific data gaps. However, for RECCAP2, we improved our overall understanding of many of the important components of the African GHG budget that will assist to inform climate policy and action.

Fossil fuel combustion, land use change and other human activities have increased the atmospheric carbon dioxide (CO2) abundance by about 50% since the beginning of the industrial age. The atmospheric CO2 growth rates would have been much larger if natural sinks in the land biosphere and ocean had not removed over half of this anthropogenic CO2. As these CO2 emissions grew, uptake by the ocean increased in response to increases in atmospheric CO2 partial pressure (pCO2). On land, gross primary production (GPP) also increased, but the dynamics of other key aspects of the land carbon cycle varied regionally. Over the past three decades, CO2 uptake by intact tropical humid forests declined, but these changes are offset by increased uptake across mid- and high-latitudes. While there have been substantial improvements in our ability to study the carbon cycle, measurement and modeling gaps still limit our understanding of the processes driving its evolution. Continued ship-based observations combined with expanded deployments of autonomous platforms are needed to quantify ocean-atmosphere fluxes and interior ocean carbon storage on policy-relevant spatial and temporal scales. There is also an urgent need for more comprehensive measurements of stocks, fluxes and atmospheric CO2 in humid tropical forests and across the Arctic and boreal regions, which are experiencing rapid change. Here, we review our understanding of the atmosphere, ocean, and land carbon cycles and their interactions, identify emerging measurement and modeling capabilities and gaps and the need for a sustainable, operational framework to ensure a scientific basis for carbon management.

Transpiration is a key process driving energy, water and thus carbon dynamics. Global T products are fundamental for understanding and predicting vegetation processes. However, validation of these transpiration products is limited, mainly due to lack of suitable datasets. We propose a method to use SAPFLUXNET, the first quality-controlled global tree sap flow database, for evaluating transpiration products at global scale. Our method is based on evaluating temporal mismatches, rather than absolute values, by standardizing both transpiration and sap flow products. We evaluate how transpiration responses to hydro-meteorological variation from the Global Land Evaporation Amsterdam Model (GLEAM), a widely used global transpiration product, compare to in-situ responses from SAPFLUXNET field data. Our results show GLEAM and SAPFLUXNET temporal trends are in good agreement, but diverge under extreme conditions. Their temporal mismatches differ depending on the magnitude of transpiration and are not random, but linked to energy and water availability. Despite limitations, we show that the new global SAPFLUXNET dataset is a valuable tool to evaluate T products and identify problematic assumptions and processes embedded in models. The approach we propose can, therefore, be the foundation for a wider use of SAPFLUXNET, a new, independent, source of information, to understand the mechanisms controlling global transpiration fluxes.

Satellite data reveal widespread changes in Earth’s vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

Terrestrial primary productivity plays a pivotal role as a forcing factor of atmospheric CO2 and drives biospheric carbon dynamics. India is one of the largest GHGs emitters, yet less is understood in carbon cycling in terrestrial ecosystems. Here we explored the trend and magnitude of gross and net productivities of India for the last two decades (2000 – 2019) by integrating satellite observation from MODIS, remote sensing-based CASA model and twenty DGVMs from the TRENDY ensemble. Preliminary results exhibited a unimodal response across the data products with an overall positive trend and a declining decadal trend for 2010 – 2019. Alongside, the SPEI drought severity index across various ecological zones indicated India was more positively sensitive to wet span than the dry. We found that the ecosystems were drastically shifting their nature to C source with a positive trend in the productivities and were mediated by the changing climate. The analysis also revealed the increasing decadal amplitude of GPP by 0.0884 Pg C/Year, NBP by 0.0096 Pg C/Year, NEP by 0.0195 Pg C/Year, NPP by 0.0448 Pg C/Year and NEE by 0.0161 Pg C/Year. CASA underestimated the magnitudes but with the temporal synchronisation of the ensemble. Seasonal variability across the agro-ecological zones was more sensitive and was an offset for the declining productivities in the primaeval forests of India. The monsoon season contributed to the interannual variability of India. Higher uncertainty in productivities was observed in the high greening areas, whereas it contradicted NBP by reflecting a stable trend. Our results underscore the nature of C variability in the terrestrial ecosystems of India; and, they indicate that C release has reacted stronger than the C uptake, which was substantially inferred from NEE across the ecological zones.

Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknow on what scales these estimates are robust. Here, we use the time-dependent Bayesian synthesis top-down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (URR) decrease significantly at decreasing spatial scales. On the continental-scale, the mean URR is about 60%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental-scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage.

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO2 emissions during the 1959-2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models collectively referred to as the TRENDY ensemble and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well TRENDY models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference datasets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.